Utilizing Large Language Models for Automating Technical Customer Support
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866907924788412416 |
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| author | Wulf, Jochen Meierhofer, Jürg |
| author_facet | Wulf, Jochen Meierhofer, Jürg |
| contents | The use of large language models (LLMs) such as OpenAI's GPT-4 in technical customer support (TCS) has the potential to revolutionize this area. This study examines automated text correction, summarization of customer inquiries and question answering using LLMs. Through prototypes and data analyses, the potential and challenges of integrating LLMs into the TCS will be demonstrated. Our results show promising approaches for improving the efficiency and quality of customer service through LLMs, but also emphasize the need for quality-assured implementation and organizational adjustments in the data ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01407 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Utilizing Large Language Models for Automating Technical Customer Support Wulf, Jochen Meierhofer, Jürg General Economics Economics The use of large language models (LLMs) such as OpenAI's GPT-4 in technical customer support (TCS) has the potential to revolutionize this area. This study examines automated text correction, summarization of customer inquiries and question answering using LLMs. Through prototypes and data analyses, the potential and challenges of integrating LLMs into the TCS will be demonstrated. Our results show promising approaches for improving the efficiency and quality of customer service through LLMs, but also emphasize the need for quality-assured implementation and organizational adjustments in the data ecosystem. |
| title | Utilizing Large Language Models for Automating Technical Customer Support |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2406.01407 |